Monetary and fical policy cooperation in a liquidity trap

We are living interesting times in terms of macroeconomic policy: the world faces big shocks and substantial challenges, and many current circumstances have no historical precedents. This means that policy makers cannot draw from experience and need to invent new policies from somewhere better than their guts. And after a few hesitations, theory is now in much better shape to answer questions from policy makers. For example, what should one do when there is a liquidity trap in a globalized economy, especially if the trap itself is globalized?

David Cook and Michael Devereux show how, and it borders on a political miracle. Not only does one need to get the cooperation of fiscal and monetary authorities (something the US is not close to acheiving) but one needs the cooperation across countries even if it entails some costs to the "winners" (something the Chinese have so far refused and the Swiss recently abandoned).

Specifically, Cook and Devereux show that with so many countries currently with negative real interest rates, we have a worldwide liquidity trap. In an open economy, the policy prescription differs from a closed economy. If there is a negative demand shock, fiscal policy needs of course to raise aggregate demand, but with a global economy, this can come from anywhere in the world and thus a coordinated fiscal policy is due. But to channel the impulse to the relevant countries, monetary policy coordination is necessary to raise interest rates in the foreign countries, even if they are in a liquidity trap. And this takes some serious courage. One can always dream.

Public consumption and the business cycle

One aspect of government purchases the current crisis has highlighted is how volatile they can be. Quite obviously, they are influenced by politics, to the point of complete reversal between massive spending and severe belt-tightening within months as in the US and the UK. But there could also be a more systematic component that is linked to the business cycle. After all, the government may be trying to improve the welfare of its constituents and for example substitute public consumption for lacking private consumption, or the same for investment.



Ruediger Bachmann and Jinhui Bai look at this using an augmented real business cycle model. They claim that 25-40% of the variance of public consumption can be accounted for by shocks to total factor productivity once implementation lags and costs of public consumption, as well as taste shocks to public vs. private consumption. I am no particular fan of taste shocks, as they are the symptoms of a modeler who is giving up on trying to explain something and simply equates the error term in the Euler equation to a shock. Then much is driven by how this shock is calibrated, in this case to match a four year electoral cycle and some data moments. When I think about shocks in this context, I think indeed about who is in power to decide on public expenditures. But that is not completely exogenous. Indeed, the state of the economy has an impact on who gets elected or reelected. And this can be calibrated without trying to match the data moments one is trying to explain.

What if the US looses its reserve currency privilege?

Since the Bretton Woods agreement in 1945, the United States have enjoyed the so-called "Exhorbitant Privilege." During the fixed exchange rate regime, the US could conduct monetary policy without regard to what was happening in other countries. The US dollar was a reserve currency, which also helped the US maintain low interest rates and a guarantee that US dollars (and Treasury bonds) would always find a buyer. With flexible exchange rates, not much has changed. But with the recent shenanigans in a Congress that considered reneging on its debt, the likelihood of this advantage changing has dramatically increased. What would the consequences of the loss of the Exhorbitant Privilege be?



Wenli Cheng and Dingsheng Zhang study this scenario using a general equilibrium model where a peripheral country (say, the Asian economies) pegs its currency to the money of a central country (say, the United States), the latter being used as the vehicle currency for international trade. In addition, the foreign exchange reserves of the periphery are invested in government bonds of the center. This means that no matter what current account deficit of the center, it is always financed by the periphery. Yet the center may be tempted to inflate it away. This limitless and to some extend free borrowing is the Exhorbitant Privilege.



Now remove it by assuming that the periphery does not want to invest in the center, either because it views the Treasury bonds are excessively risky or because it does not peg to the dollar any more. This would lead to a dramatic readjustment of the terms of trade to favor the tradable sector of the center. This decpraciation of the dollar would be more pronounced of the center is incapable of raising taxes and finances its debt with inflation. This already all sounds familiar.

Policy risk and the business cycle

The US economy seems stuck in its tracks, and many blame uncertainty about future public policy, including me. Indeed, private firms are currently sitting on a lot of cash and are making very good profits, yet they are not investing or hiring. This really looks like a wait-and-see game. But it this justification well-founded or is it just a cheap excuse to justify higher than usual profits in the face of high unemployment?

Benjamin Born and Johannes Pfeifer put some structure into these arguments by taking a standard New Keynesian model and adding uncertainty about monetary and fiscal policy. They measure this by looking at tax rates and monetary policy shocks with time-varying volatility. Previous literature already looked at the impact of aggregate uncertainty, which policy makers can do little about. But policy uncertainty is another matter. And there is hope, as Born and Pfeifer show that the impact of policy uncertainty is not that important (but much larger than uncertainty about productivity shocks) thanks to monetary policy reaction through a Taylor Rule. So that is somewhat reassuring, but then the size of the current policy uncertainty is an order of magnitude larger than when this paper was written, and monetary policy is bound by non-negative nominal interest rates.

Razor innovation in macroeconomics

Macroeconomics is sometimes like Gillette razors. There is regularly an innovative razor that happens to have more blades than the previous one. And once it gets out of hand, the new razor goes back to fundamentals and has only one blade, before the cycle starts again. In macroeconomics, there was this fad of adding more an more shocks to models until everything became very confusing and unidentifiable. So we returned to simple models (Occam's razor was the innovation) that became more powerful because of the presence of a market friction. Now, these search frictions are appearing everywhere, one-by-one or in pairs, and the latest generation of models has three frictions.

In a pair of papers, Etienne Wasmer alone and then with Nicolas Petrosky-Nadeau introduces search frictions on labor, credit and goods markets. The first is more of an exercise of style, showing it can be elegantly solved in steady-state thanks to block-recursiveness. The second paper is more interesting, as it looks at the dynamic properties of the model and shows that is can better account for the persistence of fluctuations in the data (what frictions are good at) and the volatility of labor flows. Interesting results, especially in the light of the pronounced lag in the recovery of employment in the US these days.

I am waiting for the four-friction model now.

The welfare gain from inflation targeting

It is rather well accepted that transparency is preferred for policy, because it anchors better expectations, people generally do not like uncertainty, and discretion can lead to adverse biases compared to set policy rules. Yet, the United States exhibit little transparancy, with the Federal Reserve being one of the few western central banks not to declare some explicit policy target, and fiscal policy being as uncertain as ever. That would not be a big deal if the welfare costs were low, but you can think that there are high and in the case of fiscal policy are currenctly holding back the recovery.

Giorgio Di Giorgio and Guido Traficante are taking a closer look at the welfare benefits of inflation targeting. For this they use a model where households observe policy interest rates and do not know whether their changes are due to reactions to output gaps or shocks to the inflation target. Households are sophisticated, they use a signal extraction device to estimate the latent, unobservable variable. Yet, they still face substantial costs from the uncertainty. Money is not neutral because of Rotemberg pricing, a variant of Calvo pricing. Oh well, I guess this is what you need to do to get a result with some bite.

Households know there is a policy rule that determines the interest rate from the output gap (unobservable) and the inflation target (stochastic and persistent) as well as known preference and cost-push shocks. In other words, households know a lot about the structure of the economy and the shocks, except for the policy shock, but then somehow cannot figure out what the output gap is. The central bank can, though, but then has for obscure reasons a trembling hand when it comes to set its inflation target. That seems to be quite the opposite of what I would have thought: everyone is confused about the output gap, and only the central bank knows what the inflation target is. Instead of a story of households trying to disentangle output gap and inflation target from the interest rate signal, one would have a story of a central banker not quite sure what to do given the circumstances. Too bad, this could have been an interesting paper.

Do not waste degrees of freedom with macro data

Dealing with microdata is relatively easy, as you have plenty of data points and can freely add explanatory variables with running the risk of running out of degrees of freedom. The story is different for macrodata, as series are much shorter, and one can quickly eat degrees of freedom by using lagged variables. The prime example here are the often abused vector autoregressions (VAR), that get larger and larger, and faster than new data points accumulate. The latest fad is to run regressions with time varying parameters, including in VARs, which is deadly for degrees of freedom as this is roughly equivalent to adding a boatload of dummy variables to the mix. Hence the need to be more parsimonious.

How parsimonious should one be? Joshua Chan, Gary Koop, Roberto Leon-Gonzalez and Rodney Strachan think the solution is in time-varying parsimony. The idea is that sometimes one needs a more complex model, and sometimes a few variables are sufficient. While this allows to spare degrees of freedom when one can do with few variables, this gain on paper is lost, and probably more than lost, by the implicit degrees of freedom used in selecting the right model. This is an old problem than is swept under the rug is many empirical applications, but in this case it becomes even more apparent because so many parameters and models are involved.

What is a sticky price?

An amazing amount of scholarly effort is devoted to figuring out optimal stabilization policies in developed economies. I am not convinced this effort is well-placed, as fluctuations in developing economies are much larger and long-term trends quickly swamp short-term fluctuations in welfare assessment for developed economies. The last recession in the US may make it worth to look at stabilization though.

Greg Mankiw and Matthew Weinzierl have a piece of rather pedagogical nature trying to convince us that stabilization policy is worthwhile. Their model is essentially the one that is taught to undergraduates: a two-period model with households maximizing intertemporal utility from consumption, a government, and firms that maximize discounted profits. Oddly, firms do not care about the resale value of capital in the second period, which makes investment largely irrelevant. Finally, prices are fixed the first period, but can be changed in the second period.

Beyond the pedagogical merit, can this model be used for serious policy prescriptions, which Mankiw and Weinzierl even quantify? For one, the last recession was sufficiently important that prices and wages actually adjusted down in the short term, which violates the critical premise of the model. Indeed, all what policy tries to do is undo the frictions stemming from price rigidity. Second, when prices do indeed not change in the short-term, it is presumably when it is not worth doing do so, thus policy intervention also does not seem worth it. Of course, it could be that there is a genuine Keynesian lack of demand, but this can be attacked best by dealing with what causes the lack of demand, not by creating artificial demand through government expenses. For the last recession, this would have been easing collateral constraints. Third, the model assumes a money quantity equation, which imposes a constant money velocity. I thought we all had agreed long ago this was a silly assumption.

I really do not understand the point of this paper. After all, as Mankiw likes to say on his blog, all this can already be found in his favorite textbook.

Socialist economies smooth better the cycle

Capitalism is often presented as a wild economic system where conditions are harsh as everyone fights for his survival. The fact that economic agents are not sheltered against shocks leads them to be more efficient and possibly protect themselves better against events. Incentives are not as well aligned in a socialist economy, as free-riding is more prevalent and weaker agents may be more likely to survive in such a sheltered system. The endless discussions on which system is better ultimately boil down to preferences about risk tolerance and fairness, and on which system offers higher welfare.

Bruno Amable and Karim Azizi point out that more socialist economies appear to be better at smoothing out business cycles in the aggregate. Indeed, they tend to adopt more readily Keynesian policies, which do smooth somewhat economic fluctuations, France being the prime example. But that does not yet mean these economies are better: while fluctuations are lesser, the average level may also be lower. And fluctuations may be optimal, as we have learned from the real business cycle literature. So the jury is still out.

What is a macroeconomic model?

Critics have had a field day the past couple of years claiming that Economics is not up to the task because its models are too abstract. Macroeconomics has been especially affected because the trigger of the bandwagon was a macroeconomic event, the now Great Recession. I have discussed a few bad attempts at criticism, which were usually bad because ill-informed and because they could not offer any viable alternative.

The latest salvo comes from Hashem Pesaran and Ron Smith. They have several arguments. The first is that macroeconomic modeling of the DSGE brand insists too much on internal consistency and should allow more degrees of freedom to fit the data. Pesaran and Smith should first specify what the goal of the model is before criticizing the approach. If it is short-term forecasting, then go ahead with a purely statistical approach on macro data. If you want policy advice, you need something that withstands the Lucas Critique, and strong micro-foundations is then the way to go. But without a given purpose, any criticism is moot.

Pesaran and Smith, given their track record, are of course strong advocates of purely statistical methods. Throw every possible series in a regression, and see what sticks. I am not saying this cannot be useful, it allows to establish relationships in the data and I regularly report on such results, but this does not allow you to explain things. For this, you need some structure and theory provides you that. This brings me to the title of this post. There appears to be some disagreement about the meaning of the word "model." To me, model is a set of relationships established by theory that can then be used on data, for policy experiments, etc. For Pesaran and Smith, a model is a set of aggregate data series that are used in a statistical analysis of some kind (VAR, non-parametric, etc.). If we cannot agree what we are talking about, of course there will be endless and fruitless discussions.

For example, they are not the first to criticize DSGE models for failing to include housing, finance and the external sector. Well, models (the way I see them) are abstractions, and you do not want to include everything them, or you cannot understand, interpret or do something useful with. It is so across all sciences. You build a model to answer a particular question, and you give it the necessary bells and whistles. The fact that most DSGE models did not include housing and bank liquidity is not a failure of DSGE modelling, it is a failure of recognizing what questions could be important in the future and this is damn hard to do properly.

Pesaran and Smith's solution to what they call the straightjacket of DSGE is to throw all these missing variables in a regression. Essentially, they want to bypass the discipline that theory imposes by letting the data speak. Again this is OK if you want to explore and find relationships, but this is not going to be very useful if you want to explain what is going on. Specifically, they advocate using vector autoregressions (VAR). As they complain that DSGE use representative agents when heterogeneity matters, they call for the use of data from several countries in the VAR, a rather strange argument, but I suppose this is because they are limited to aggregate data (and they neglect all the DSGE models using household level data...). In a statistical sense, the big problem is now that one quickly runs out degrees of freedom, as one has only so many time periods, and every additional variable eats degrees of freedom at a quadratic rate (times the number of lags). The other problem is that interpreting the resulting errors ("shocks") becomes difficult. One is then limited to vague notions like demand and supply shocks, much like in factor analysis. But at least Pesaran and Smith acknowledge that theory can be useful in selecting, say, long-term restrictions.

PS: I gave much of the same arguments in the discussion of a paper by David Hendry. Pesaran appears to be more knowledgeable of DSGE and is more willing to use theory to guide empirics. Unfortunately, Pesaran has the same habit of abusing self-citations, 12 out of 32.

The shoe-leather cost of inflation is minimal

One popular way to justify the introduction of monetary frictions in macroeconomic models is to assume that there is some cost associated to changing cash holdings, ATM fees or more generally "shoe-leather" cost. Whether these cost matter at all is controversial and settling this requires a two-pronged approach: first find empirically how large these costs are, and second demonstrate that the costs are large enough to matter in a reasonable model.

Alessandro Calza and Andrea Zaghini estimate the shoe-leather cost for the US. This is by far not the first time this is performed, by it can be worth it as data change, and in this case one can suspect that transaction costs indeed have gone down over a few decades. But there is one critical aspect that they take into account: most on US M1 is not held domestically, and this share has increased to currently 60%. Ignoring this seriously biases estimates, first because it overstates domestic demand and second because the shoe-leather cost stemming from inflation is largely borne by foreigners. At an inflation rate of 10%, the cost amounts to negligible 0.05% of total income. At lower inflation rates, it is even negative thanks to foreigners giving up real resources to acquire US money. In other words, you cannot build a monetary theory on this,

An empirical nail in the coffin of Calvo pricing

I have never been shy about the fact that I am no fan of Calvo pricing as commonly practiced in New-Keynesian model. I have presented on this blog at several occasions theoretical evidence against it, as well as empirical evidence that price are either not rigid enough to matter, or that the way they change is not consistent with Calvo pricing.

Sascha Becker pushes the argument further. There is ample evidence that the relationship between inflation and price dispersion is U-shaped: At very low inflation, the is maximal dispersion. It decreases and increases with higher inflation. Monetary models can explain this, but very differently. Money search models require market power for this to happen. New-Keynesian models with Calvo pricing need sticky prices. Using monthly data from price level indices for 38 sectors in 12 countries over 13 years, Becker looks for sectors where there is more or less competition and more or less price stickiness. The U-shape is systematically found where there is more market power, but not where there is price stickiness...

The unnecessary problems of the Euro

European leaders are currently struggling over a package to save the Euro, pouring large amounts of money into funds that should stabilize the fiscal situation in Greece, Portugal, Ireland and potentially other countries. It seems to me that this is a completely unnecessary problem, and all this grief could have easily been avoided with a simple change in policy.

Just look at what is happening in the United States. Several states are in serious financial difficulties and, as several times in the past, California is considering issuing IOUs, thereby essentially declaring it is insolvent. Is there any expectation that other states or the federal government will rush to California's aid because the dollar is threatened? Of course not, despite the fact that California is the largest state in the Union.

It should be the same for the Euro. None of the member countries can monetize its debt on its own, and the only reason that the Euro is threatened is that markets have an expectation that other countries will rush to help, thereby sending a message that monetary policy could be influenced by what is happening in those small countries. And why is this belief well anchored? Because European indeed rush to help (talk about a nice example of self-fulfilling expectations) and because of this silly concept that all national debt in Europe is fungible (talk about a nice example of the tragedy of the commons). Now of course it is a bit late to rectify those beliefs, but had it been clear no rescue package were in sight, those countries would probably not taken such a risky fiscal path in the first place (talk about a nice example of moral hazard). I guess that those silly policy decisions all boil down to European politics, once more (talk about a nice example where economists' advice has been ignored, and they will get blamed for it anyway).

Price points, good diversity and price rigidity

Much of the real impact of monetary policy hinges on some sort of rigidity in some prices. As regular readers must have noticed, I am not convinced I am not convinced that prices a rigid to the point that it matters, and I am particularly appalled how price rigidity is introduced in theoretical models. Let us have a look at some of latest research on price rigidity.

Edward Knottek uses supermarket scanner to find that price points are much more important than menu costs in determining prices. Price points are for example prices ending in 9, which make up 60% of retail prices. He also finds that in all but 10% of cases, prices return to the previous level after a sale. These two facts cannot be reconciled with menu costs being of relevance. Yet menu costs are the foundation, explicitly or implicitly of almost all models of price rigidity.

Saroj Bhattarai and Raphael Schoenle use producer prices and establish interesting patterns in decisions to change prices. They find that firms with a large variety of goods change prices more frequently, but by smaller amounts. If they change a price, they are more likely to decrease it, and the variance of positive price changes is larger. They also find that for a model to replicate such facts, one needs firm-specific menu costs and state-dependent pricing. This is definitely not Calvo pricing.

The investment-consumption correlation

In the analysis of business cycles, investment-specific technology (IST) shocks are the new rage. A few papers now have shown that they explain better business cycles than the classical shocks to total factor productivity ("technology"). Specifically, IST shocks are perturbations to how efficiently investment translates into productive capital. There is one problem, though, and it is a major one: in response to IST shocks, investment and consumption move in opposite directions, while the data clearly indicate they are positively correlated.

Francesco Furlanetto and Martin Seneca study under which circumstances a positive correlation could be obtained. They find that you need the following: non-separability of consumption and leisure in utility and nominal price rigidity (à la Calvo, sigh...). Remove any of the two, and the result crumbles. Thus it seems essential to make a point whether these two assumptions make sense. Regular readers know that I do not believe Calvo pricing is an appropriate way of looking at price rigidity, provided that it even is significant to matter economically in the first place. It is not clear that another pricing mechanism would yield the same result. And regarding non-separability of leisure and consumption, I have no evidence whether this is a valid assumption, and the authors do not help me. And what about other shocks? Those typically lead to positive correlations between consumption and investment, and include such shocks may help relaxing the requirements identified by Furlanetto and Seneca.

Should food prices influence monetary policy?

Central banks care about inflation, but not about the inflation people care about. Because energy prices are volatile, they are not included in the price index a central bank typically looks at, because including it would made the indicator less informative. At they also include food prices, because those fluctuate a lot as well, in particular because of seasons. That can make sense for a rich country, where food represents only a small portion of the budget. For poorer countries, excluding food is more controversial.

Luis Catão and Roberto Chang note that world food prices seem to cause worldwide inflation, and this should have implications for inflation in small open economies that take food prices as given. The question is then whether central banks should react to such terms of trade shocks. For a net food importer, Catão and Chang find that including food in the price index relevant to the central bank is welfare enhancing. The reason is that if food has a larger weight domestically than in the rest of the world, the real exchange rate and the terms of trade can move in opposite directions following a food price shocks. The welfare improvement comes from a change in the correlations in aggregates leading to smoother consumption, but it possibly results in higher inflation and higher volatility of output and employment. It is thus not obvious including food prices would then be an easy sell.